 A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical ScalingLouis T. Mariano, Daniel F. McCaffrey and J. R. Lockwood Journal of Educational and Behavioral Statistics, 2010, vol. 35, issue 3, 253-279 Abstract: There is an increasing interest in using longitudinal measures of student achievement to estimate individual teacher effects. Current multivariate models assume each teacher has a single effect on student outcomes that persists undiminished to all future test administrations (complete persistence [CP]) or can diminish with time but remains perfectly correlated (variable persistence [VP]). However, when state assessments do not use a vertical scale or the evolution of the mix of topics present across a sequence of vertically aligned assessments changes as students advance in school, these assumptions of persistence may not be consistent with the achievement data. We develop the “generalized persistence†(GP) model, a Bayesian multivariate model for estimating teacher effects that accommodates longitudinal data that are not vertically scaled by allowing less than perfect correlation of a teacher’s effects across test administrations. We illustrate the model using mathematics assessment data. Keywords: teacher effects; value-added models; vertical scaling; Bayesian methods (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:35:y:2010:i:3:p:253-279 Access Statistics for this article More articles in Journal of Educational and Behavioral Statistics |
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